Automatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders.

Raheel Zafar,Abdul Qayyum,Wajid Mumtaz

doi:10.31083/j.jin.2019.03.164

Abstract

In the electroencephalogram recorded data are often confounded with artifacts, especially in the case of eye blinks. Different methods for artifact detection and removal are discussed in the literature, including automatic detection and removal. Here, an automatic method of eye blink detection and correction is proposed where sparse coding is used for an electroencephalogram dataset. In this method, a hybrid dictionary based on a ridgelet transformation is used to capture prominent features by analyzing independent components extracted from a different number of electroencephalogram channels. In this study, the proposed method has been tested and validated with five different datasets for artifact detection and correction. Results show that the proposed technique is promising as it successfully extracted the exact locations of eye blinking artifacts. The accuracy of the method (automatic detection) is 89.6% which represents a better estimate than that obtained by an extreme machine learning classifier.

Highlights

The electroencephalogram (EEG) is a standard modality for the study of neural activity by direct measurement from the scalp
Different EEG datasets were used to remove artifacts based on a sparse representation technique that employed 20 independent component analysis (ICA) components for their measurement and detection
Sparse representation is a powerful technique for extracting prominent features of a pattern from the ICA component

Summary

Introduction

The electroencephalogram (EEG) is a standard modality for the study of neural activity by direct measurement from the scalp. EEG offers portability, low cost, and ease of availability, and relatively high temporal resolution. For these reasons, EEG is popular in different brain applications such as the brain-computer interface (BCI) (Nezamfar et al, 2011; Robinson et al, 2011), decoding (Crouzet et al, 2015; Zafar et al, 2017) and seizure detection (Cecotti and Graser, 2011; Zhou, 2014). EEG data is confounded with various artifacts that may lead to serious misinterpretations, in clinical studies. An artifact is typically unwanted noisy data that must be removed before further processing

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